Journal article
A comprehensive AI model development framework for consistent Gleason grading
- Abstract:
-
Background: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. Methods: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Publisher copy:
- 10.1038/s43856-024-00502-1
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Bibliographic Details
- Publisher:
- Nature Research
- Journal:
- communications medicine More from this journal
- Volume:
- 4
- Issue:
- 1
- Article number:
- 84
- Publication date:
- 2024-05-09
- Acceptance date:
- 2024-04-17
- DOI:
- EISSN:
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2730-664X
- ISSN:
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2730-664X
Item Description
- Language:
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English
- Source identifiers:
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1959139
- Deposit date:
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2024-07-20
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